Primary Convener:
Melissa Allen Dumas, Oak Ridge National Lab

Convener:
Bhartendu Pandey, Oak Ridge National Lab
Pouya Vahmani, University of California Los Angeles
Deeksha Rastogi, Oak Ridge National Laboratory

Early Career Convener:
Ifeanyichukwu Nduka, University of Texas at Austin

Chair:
Deeksha Rastogi, Oak Ridge National Laboratory
Timothy Jiang, Pacific Northwest National Laboratory
Haoran Niu, Oak Ridge National Laboratory

Urban changes—demographic shifts, new/amplified migration patterns, built-up land expansion, infrastructural changes, local-to-regional environmental changes—are driving and responding to global environmental changes. The unprecedented scale and rate of these changes and their societal impacts call for transformative approaches to understand urban systems and highlight solutions addressing multidimensional tradeoffs and synergies. State-of-the-art artificial intelligence (AI) approaches highlight tremendous potential to transform urban systems science through advancements in novel measurement, modeling and simulation, and decision support systems. This session invites innovative studies that conceptualize, develop, and/or apply artificial intelligence techniques to advance our understanding of urban systems. Relevant topics include: multimodal foundational models for urban systems; AI for urban socioeconomic and environmental measurements; AI-based urban climate emulators; AI in modeling and forecasting urban parameters: land use/cover, morphology, demographics, economic activity, transportation, climate, air pollution, heat, flooding, energy/water/materials demand; AI-based urban complexity analysis; and metrics and benchmarks for AI models’ evaluation and trustworthiness assessment.

Index Terms
1622 Earth system modeling
1630 Impacts of global change
1632 Land cover change
1942 Machine learning

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI
Open Science and Open Data
Global Impacts‚ Solutions‚ & Policies

Neighborhoods:
3. Earth Covering